Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed, Umer; Shah, Syed Yaseen; Ahmad, Jawad; Imran, Muhammad Ali; Abbasi, Qammer H.; Shah, Syed Aziz

doi:10.1016/j.jpha.2021.12.006

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed, Umer; Shah, Syed Yaseen; Ahmad, Jawad; Imran, Muhammad Ali; Abbasi, Qammer H.; Shah, Syed Aziz

Authors

Umer Saeed

Syed Yaseen Shah

Dr Jawad Ahmad J.Ahmad@napier.ac.uk
Lecturer

Muhammad Ali Imran

Qammer H. Abbasi

Syed Aziz Shah

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

Citation

Saeed, U., Shah, S. Y., Ahmad, J., Imran, M. A., Abbasi, Q. H., & Shah, S. A. (2022). Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. Journal of Pharmaceutical Analysis, 12(2), 193-204. https://doi.org/10.1016/j.jpha.2021.12.006

Journal Article Type	Review
Acceptance Date	Dec 30, 2021
Online Publication Date	Jan 5, 2022
Publication Date	2022-04
Deposit Date	Jul 15, 2022
Publicly Available Date	Jul 15, 2022
Journal	Journal of Pharmaceutical Analysis
Print ISSN	2095-1779
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	12
Issue	2
Pages	193-204
DOI	https://doi.org/10.1016/j.jpha.2021.12.006
Keywords	Artificial intelligence, Non-invasive healthcare, Machine learning, Non-contact sensing, COVID-19
Public URL	http://researchrepository.napier.ac.uk/Output/2889069